Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions weaken the assumptions of traditional urban-centric localization methods, which assume that consistent features arise from unique structural patterns, necessitating forest-centric solutions to achieve robustness in these environments. To address these challenges, we propose TreeLoc, a LiDAR-based global localization framework for forests that handles place recognition and 6-DoF pose estimation. We represent scenes using tree stems and their Diameter at Breast Height (DBH), which are aligned to a common reference frame via their axes and summarized using the tree distribution histogram (TDH) for coarse matching, followed by fine matching with a 2D triangle descriptor. Finally, pose estimation is achieved through a two-step geometric verification. On diverse forest benchmarks, TreeLoc outperforms baselines, achieving precise localization. Ablation studies validate the contribution of each component. We also propose applications for long-term forest management using descriptors from a compact global tree database. TreeLoc is open-sourced for the robotics community at https://github.com/minwoo0611/TreeLoc.

翻译：可靠的定位对于森林导航至关重要，因为GPS信号在此类环境中常出现衰减，而激光雷达测量则具有重复性、易受遮挡且结构复杂的特点。这些条件削弱了传统以城市为中心的定位方法的基本假设——后者依赖于独特结构模式所产生的稳定特征，因此需要针对森林环境设计专门的解决方案以实现鲁棒性。为应对这些挑战，我们提出了TreeLoc，一种基于激光雷达的森林全局定位框架，能够同时处理地点识别与六自由度姿态估计。我们采用树干及其胸径（DBH）作为场景表征要素，通过树干轴线将其对齐至统一参考坐标系，并利用树木分布直方图（TDH）进行粗匹配，随后使用二维三角形描述符进行精细匹配。最终通过两步几何验证实现姿态估计。在多样化的森林基准测试中，TreeLoc性能优于基线方法，实现了精确的定位。消融实验验证了各模块的有效性。我们还提出了利用紧凑型全球树木数据库中的描述符进行长期森林管理的应用方案。TreeLoc已通过https://github.com/minwoo0611/TreeLoc开源，供机器人学研究社区使用。